Research graph
References from EMCS-PGNN: Physics-guided neural network-based enhanced Monte Carlo simulation for high-dimensional reliability analysis. Local targets link to admitted publications; unresolved targets remain external evidence.
Fourth-order isogeometric phase-field modeling of dynamic brittle fracture: numerical study and comparison with second-order models
10.1016/j.cma.2025.118513 · 2026 · External reference
Intelligent-inspired framework for fatigue reliability evaluation of offshore wind turbine support structures under hybrid uncertainty
10.1016/j.oceaneng.2024.118213 · 2024 · External reference
A novel finite-strain mixed isogeometric collocation formulation for hyperelastic geometrically exact beams
10.1016/j.cma.2025.118641 · 2026 · External reference
Time series clustering adaptive enhanced method for time-dependent reliability analysis and design optimization
10.1016/j.cma.2025.118099 · 2025 · External reference
A regular-vine copula-based evidence theory model for structural reliability analysis involving multidimensional parameter correlation
10.1016/j.cma.2025.118152 · 2025 · External reference
A fully explicit isogeometric collocation formulation for the dynamics of geometrically exact beams
10.1016/j.cma.2024.117283 · 2024 · External reference
Isogeometric collocation for locking-free large deflection analysis of geometrically exact beams with intrinsic formulation
10.1016/j.cma.2025.118282 · 2025 · External reference
Structural design optimization under stochastic excitations considering first-passage probability constraint based on dimension-reduced probability density evolution equation
10.1016/j.ress.2025.111378 · 2025 · External reference
A unified anisotropic VDM–PFM theory for failure in fiber-reinforced composite materials
10.1016/j.jmps.2026.106739 · 2026 · External reference
A unified variational damage model and an efficient length scale insensitive phase-field model
10.1016/j.jmps.2025.106494 · 2026 · External reference
An active learning method combining MRBF model and dimension-reduction importance sampling for reliability analysis with high dimensionality and very small failure probability
10.1016/j.ress.2025.111107 · 2025 · External reference
A new uncertainty reduction-guided single-loop Kriging coupled with subset simulation for time-dependent reliability analysis
10.1016/j.ress.2025.111065 · 2025 · External reference
Uniform importance sampling with rejection control for structural reliability analysis
10.1016/j.cma.2024.117707 · 2025 · External reference
Combination line sampling for structural reliability analysis
10.1016/j.strusafe.2020.102025 · 2021 · External reference
Reliability and global sensitivity analysis based on importance directional sampling and adaptive Kriging model
10.1007/s00158-023-03584-y · 2023 · External reference
Enhancements and benchmarking of MCMC algorithms for subset simulation in structural reliability
10.1016/j.probengmech.2025.103809 · 2025 · External reference
Support vector regression-based enhanced quasi-Monte Carlo simulation for efficient turbine runner reliability analysis
10.1108/ijsi-04-2026-0061 · 2026 · External reference
Reliability analysis of wind turbine gearboxes: past, progress and future prospects
10.1108/ijsi-08-2024-0129 · 2025 · External reference
AK-Gibbs: an active learning Kriging model based on Gibbs importance sampling algorithm for small failure probabilities
10.1016/j.cma.2024.116992 · 2024 · External reference
A novel hybrid adaptive Kriging and water cycle algorithm for reliability-based design and optimization strategy: application in offshore wind turbine monopile
10.1016/j.cma.2023.116083 · 2023 · External reference
Reliability evaluation of a multi-state system with dependent components and imprecise parameters: a structural reliability treatment
10.1016/j.ress.2024.110240 · 2024 · External reference
Semi-supervised cross-domain fault diagnosis via contrastive pre-training and annotation-efficient alignment strategy
2026 · External reference
A novel machine learning method for multiaxial fatigue life prediction: improved adaptive neuro-fuzzy inference system
10.1016/j.ijfatigue.2023.108007 · 2024 · External reference
Probabilistic modeling of threshold stress intensity factor for fatigue endurance reliability prediction
10.1016/j.probengmech.2023.103417 · 2023 · External reference
Rare event probability evaluation for static and dynamic structures based on direct probability integral method
2025 · External reference
Unified framework for stochastic dynamic responses and system reliability analysis of long-span cable-stayed bridges under near-fault ground motions
10.1016/j.engstruct.2024.119061 · 2025 · External reference
Active Kriging-based conjugate first-order reliability method for highly efficient structural reliability analysis using resample strategy
10.1016/j.cma.2024.116863 · 2024 · External reference
Active Kriging-based uniform importance sampling with rejection control for highly efficient and accurate structural reliability analysis
10.1016/j.ress.2026.112513 · 2026 · External reference
Dimension-reduced Chapman-Kolmogorov equation for high-dimensional stochastic dynamical systems
10.1016/j.cma.2025.118433 · 2026 · External reference
Variational Bayesian data assimilation with time-varying multi-physics-informed neural network for solving dimension-reduced probability density evolution equation
10.1016/j.ress.2026.112216 · 2026 · External reference
Reliability analysis of corroding pipelines by enhanced Monte Carlo simulation
10.1016/j.ijpvp.2016.04.003 · 2016 · External reference
System reliability analysis by enhanced Monte Carlo simulation
10.1016/j.strusafe.2009.02.004 · 2009 · External reference
Optimizing uncertainty estimation in Enhanced Monte Carlo methods
10.1016/j.strusafe.2025.102617 · 2025 · External reference
Support vector regression-based enhanced quasi-Monte Carlo simulation for efficient turbine runner reliability analysis
10.1108/ijsi-04-2026-0061 · 2026 · External reference
Probabilistic modeling of uncertainties in reliability analysis of mid- and high-strength steel pipelines under hydrogen-induced damage
10.1108/ijsi-10-2024-0177 · 2025 · External reference
Hybrid enhanced Monte Carlo simulation coupled with advanced machine learning approach for accurate and efficient structural reliability analysis
10.1016/j.cma.2021.114218 · 2022 · External reference
EMCS-SVR: hybrid efficient and accurate enhanced simulation approach coupled with adaptive SVR for structural reliability analysis
10.1016/j.cma.2022.115499 · 2022 · External reference
A physics-informed deep learning framework for solving forward and inverse problems based on Kolmogorov–Arnold Networks
10.1016/j.cma.2024.117518 · 2025 · External reference
PENCO: a physics–energy–numerics–consistent operator for 3D phase field modeling
10.1016/j.cma.2026.118862 · 2026 · External reference
Physics-informed neural network classification framework for reliability analysis
10.1016/j.eswa.2024.125207 · 2024 · External reference
Physics-informed neural networks: a deep learning framework for solving forward and inverse problems involving nonlinear partial differential equations
10.1016/j.jcp.2018.10.045 · 2019 · External reference
PINN-FORM: a new physics-informed neural network for reliability analysis with partial differential equation
10.1016/j.cma.2023.116172 · 2023 · External reference
Variational Physics-informed Neural Operator (VINO) for solving partial differential equations
10.1016/j.cma.2025.117785 · 2025 · External reference
An energy approach to the solution of partial differential equations in computational mechanics via machine learning: concepts, implementation and applications
10.1016/j.cma.2019.112790 · 2020 · External reference
First-order reliability method based on Harris Hawks Optimization for high-dimensional reliability analysis
10.1007/s00158-020-02587-3 · 2020 · External reference
First-order reliability method based on Harris Hawks Optimization for high-dimensional reliability analysis
10.1007/s00158-020-02587-3 · ExternalCitation · doi-reference
Reliability and global sensitivity analysis based on importance directional sampling and adaptive Kriging model
10.1007/s00158-023-03584-y · ExternalCitation · doi-reference
An energy approach to the solution of partial differential equations in computational mechanics via machine learning: concepts, implementation and applications
10.1016/j.cma.2019.112790 · ExternalCitation · doi-reference
Hybrid enhanced Monte Carlo simulation coupled with advanced machine learning approach for accurate and efficient structural reliability analysis
10.1016/j.cma.2021.114218 · ExternalCitation · doi-reference
EMCS-SVR: hybrid efficient and accurate enhanced simulation approach coupled with adaptive SVR for structural reliability analysis
10.1016/j.cma.2022.115499 · ExternalCitation · doi-reference
A novel hybrid adaptive Kriging and water cycle algorithm for reliability-based design and optimization strategy: application in offshore wind turbine monopile
10.1016/j.cma.2023.116083 · ExternalCitation · doi-reference
PINN-FORM: a new physics-informed neural network for reliability analysis with partial differential equation
10.1016/j.cma.2023.116172 · ExternalCitation · doi-reference
Active Kriging-based conjugate first-order reliability method for highly efficient structural reliability analysis using resample strategy
10.1016/j.cma.2024.116863 · ExternalCitation · doi-reference
AK-Gibbs: an active learning Kriging model based on Gibbs importance sampling algorithm for small failure probabilities
10.1016/j.cma.2024.116992 · ExternalCitation · doi-reference
A fully explicit isogeometric collocation formulation for the dynamics of geometrically exact beams
10.1016/j.cma.2024.117283 · ExternalCitation · doi-reference
A physics-informed deep learning framework for solving forward and inverse problems based on Kolmogorov–Arnold Networks
10.1016/j.cma.2024.117518 · ExternalCitation · doi-reference
Uniform importance sampling with rejection control for structural reliability analysis
10.1016/j.cma.2024.117707 · ExternalCitation · doi-reference
Variational Physics-informed Neural Operator (VINO) for solving partial differential equations
10.1016/j.cma.2025.117785 · ExternalCitation · doi-reference
Time series clustering adaptive enhanced method for time-dependent reliability analysis and design optimization
10.1016/j.cma.2025.118099 · ExternalCitation · doi-reference
A regular-vine copula-based evidence theory model for structural reliability analysis involving multidimensional parameter correlation
10.1016/j.cma.2025.118152 · ExternalCitation · doi-reference
Isogeometric collocation for locking-free large deflection analysis of geometrically exact beams with intrinsic formulation
10.1016/j.cma.2025.118282 · ExternalCitation · doi-reference
Dimension-reduced Chapman-Kolmogorov equation for high-dimensional stochastic dynamical systems
10.1016/j.cma.2025.118433 · ExternalCitation · doi-reference
Fourth-order isogeometric phase-field modeling of dynamic brittle fracture: numerical study and comparison with second-order models
10.1016/j.cma.2025.118513 · ExternalCitation · doi-reference
A novel finite-strain mixed isogeometric collocation formulation for hyperelastic geometrically exact beams
10.1016/j.cma.2025.118641 · ExternalCitation · doi-reference
PENCO: a physics–energy–numerics–consistent operator for 3D phase field modeling
10.1016/j.cma.2026.118862 · ExternalCitation · doi-reference
Unified framework for stochastic dynamic responses and system reliability analysis of long-span cable-stayed bridges under near-fault ground motions
10.1016/j.engstruct.2024.119061 · ExternalCitation · doi-reference
Physics-informed neural network classification framework for reliability analysis
10.1016/j.eswa.2024.125207 · ExternalCitation · doi-reference
A novel machine learning method for multiaxial fatigue life prediction: improved adaptive neuro-fuzzy inference system
10.1016/j.ijfatigue.2023.108007 · ExternalCitation · doi-reference
Reliability analysis of corroding pipelines by enhanced Monte Carlo simulation
10.1016/j.ijpvp.2016.04.003 · ExternalCitation · doi-reference
Physics-informed neural networks: a deep learning framework for solving forward and inverse problems involving nonlinear partial differential equations
10.1016/j.jcp.2018.10.045 · ExternalCitation · doi-reference
A unified variational damage model and an efficient length scale insensitive phase-field model
10.1016/j.jmps.2025.106494 · ExternalCitation · doi-reference
A unified anisotropic VDM–PFM theory for failure in fiber-reinforced composite materials
10.1016/j.jmps.2026.106739 · ExternalCitation · doi-reference
Intelligent-inspired framework for fatigue reliability evaluation of offshore wind turbine support structures under hybrid uncertainty
10.1016/j.oceaneng.2024.118213 · ExternalCitation · doi-reference
Probabilistic modeling of threshold stress intensity factor for fatigue endurance reliability prediction
10.1016/j.probengmech.2023.103417 · ExternalCitation · doi-reference
Enhancements and benchmarking of MCMC algorithms for subset simulation in structural reliability
10.1016/j.probengmech.2025.103809 · ExternalCitation · doi-reference
Reliability evaluation of a multi-state system with dependent components and imprecise parameters: a structural reliability treatment
10.1016/j.ress.2024.110240 · ExternalCitation · doi-reference
A new uncertainty reduction-guided single-loop Kriging coupled with subset simulation for time-dependent reliability analysis
10.1016/j.ress.2025.111065 · ExternalCitation · doi-reference
An active learning method combining MRBF model and dimension-reduction importance sampling for reliability analysis with high dimensionality and very small failure probability
10.1016/j.ress.2025.111107 · ExternalCitation · doi-reference
Structural design optimization under stochastic excitations considering first-passage probability constraint based on dimension-reduced probability density evolution equation
10.1016/j.ress.2025.111378 · ExternalCitation · doi-reference
Variational Bayesian data assimilation with time-varying multi-physics-informed neural network for solving dimension-reduced probability density evolution equation
10.1016/j.ress.2026.112216 · ExternalCitation · doi-reference
Active Kriging-based uniform importance sampling with rejection control for highly efficient and accurate structural reliability analysis
10.1016/j.ress.2026.112513 · ExternalCitation · doi-reference
System reliability analysis by enhanced Monte Carlo simulation
10.1016/j.strusafe.2009.02.004 · ExternalCitation · doi-reference
Combination line sampling for structural reliability analysis
10.1016/j.strusafe.2020.102025 · ExternalCitation · doi-reference
Optimizing uncertainty estimation in Enhanced Monte Carlo methods
10.1016/j.strusafe.2025.102617 · ExternalCitation · doi-reference
Support vector regression-based enhanced quasi-Monte Carlo simulation for efficient turbine runner reliability analysis
10.1108/ijsi-04-2026-0061 · ExternalCitation · doi-reference
Reliability analysis of wind turbine gearboxes: past, progress and future prospects
10.1108/ijsi-08-2024-0129 · ExternalCitation · doi-reference
Probabilistic modeling of uncertainties in reliability analysis of mid- and high-strength steel pipelines under hydrogen-induced damage
10.1108/ijsi-10-2024-0177 · ExternalCitation · doi-reference
